| تعداد نشریات | 284 |
| تعداد نشریات سازمان | 82 |
| تعداد شماره‌ها | 3,036 |
| تعداد مقالات | 33,971 |
| تعداد مشاهده مقاله | 45,654,404 |
| تعداد دریافت فایل اصل مقاله | 76,208,219 |
مناسبترین شاخصهای گیاهی مستخرج از تصاویر ماهوارهای برای بررسی تأثیر متغیرهای اقلیمی بر جنگل‌های زاگرس شمالی | ||
| تحقیقات جنگل و صنوبر ایران | ||
| دوره 28، شماره 4 - شماره پیاپی 82، اسفند 1399، صفحه 335-350 اصل مقاله (1.73 M) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2020.343318.1944 | ||
| نویسندگان | ||
| الهه معروف‌زاده1؛ پدرام عطارد* 2؛ علی قاسمی3 | ||
| 1دانشجوی دکتری جنگل‌شناسی و اکولوژی جنگل، گروه جنگل‌‌داری و اقتصاد جنگل، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 2استاد، گروه جنگل‌‌داری و اقتصاد جنگل، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 3کارشناس ارشد آبخیزداری، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| چکیده | ||
| هدف از پژوهش پیش‌رو، انتخاب مناسبترین شاخصهای پوشش گیاهی مستخرج از تصاویر ماهوارهای برای مطالعه اثر تغییر متغیرهای اقلیمی بر پوشش جنگلی زاگرس شمالی در شهرستان سردشت بود. برای این منظور با استفاده از تصاویر سری لندست طی دوره 1988 تا 2017، ابتدا 25 شاخص گیاهی محاسبه شد. سپس با کاربرد آنالیز مؤلفههای اصلی (PCA)، نه شاخص گیاهی مؤثرتر شامل هفت شاخص در محور اول و دو شاخص در محور دوم انتخاب شدند. این دو محور عاملی، حدود 91 درصد از واریانس شاخصهای مورد بررسی را تبیین کردند. با به‌کارگیری دادههای ایستگاه سینوپتیک سردشت، ضریب‌های همبستگی خطی بین شاخصهای منتخب و متغیرهای اقلیمی بررسی شد. نتایج نشان داد که شاخصهای محورهای اول و دوم به‌ترتیب با بارش و دمای فصل بهار، همبستگی مستقیم و معکوس داشتند. طی دوره 30ساله، شاخص پوشش گیاهی متعادل‌شده خاک (Soil Adjusted Vegetation Index, SAVI) با بارش فصل بهار، 49 درصد و شاخص پوشش گیاهی نسبت قرمز سبز (Red Green Ratio Index, RGRI) با دمای متوسط فصل بهار، 51 درصد همبستگی نشان دادند. به‌سبب اثرات همسان بارش و دمای فصل بهار در دهه اول، رابطه‌های رگرسیونی بین شاخصهای SAVI و RGRI با بارش و دمای فصل بهار، 84 و 51 درصد همبستگی داشتند، در حالیکه طی دهههای دوم و سوم، ضریب همبستگی هر دو شاخص به‌ترتیب به کمتر از 50 و 10 درصد کاهش یافت. با توجه به ویژگیهای منطقه مورد مطالعه، دخیل بودن چندین عامل مؤثر بر پوشش جنگلی و نیز اثرات متضاد یا همسان متغیرهای اقلیمی بر پوشش جنگلی، می‌توان نتیجه گرفت که شاخصهای مذکور به‌منظور بررسی تأثیر تغییر متغیرهای اقلیمی بر جنگل‌های زاگرس شمالی، انطباق و همبستگی کم تا متوسطی دارند. | ||
| کلیدواژه‌ها | ||
| آنالیز مؤلفههای اصلی؛ پوشش جنگل؛ سردشت؛ لندست | ||
| عنوان مقاله [English] | ||
| Appropriate plant indicators derived from satellite images to investigate the impact of climatic parameters on forest cover in Northern Zagros, Iran | ||
| نویسندگان [English] | ||
| E. Maroufzade1؛ P. Attarod2؛ A. Ghasemi3 | ||
| 1Ph.D. Candidate of Silviculture and Forest Ecology, Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran | ||
| 2Prof., Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran | ||
| 3M.Sc. Graduated of Watershed Management, Faculty of Natural Resources, University of Tehran, Karaj, Iran | ||
| چکیده [English] | ||
| This study aimed at selecting the most appropriate vegetation indices extracted from satellite images to study the effect of climate change on forest cover in the Sardasht, W. Azerbaijan province, Iran. Therefore, 25 plant indices were initially calculated from Landsat time-series images during 1988-2017, followed by applying principal component analysis (PCA), which led to the selection of nine more effective plant indices. The selected indices included seven indices in the first axis and two in the second axis. These two axes explained ca. 91% of the variance of the studied indicators. In addition, linear correlation coefficients between selected indices and climatic parameters were investigated using the data of the Sardasht synoptic station. The results showed that the indicators of the first and second axes were directly and inversely correlated with spring rainfall and temperature, respectively. The Soil Adjusted Vegetation Index (SAVI) showed 49% correlation with spring rainfall, while the Red Green Ratio Index (RGRI) revealed 51% correlation with the average spring temperature during the 30-year period. The association of SAVI and RGRI indices with precipitation and temperature in the last three decades showed a correlation of 84% and 51%, respectively, mainly due to the matching effects of precipitation and spring temperature in the first decade. However, correlation coefficients of both indices decreased to less than 50 and 10%, respectively, in the second and third decades. According to 1) the characteristics of the study area and the involvement of other factors that affect forest cover as well as 2) the opposite or similar effects of climate parameters on forest cover, application of the mentioned indicators to survey the effect of climate change on the Northern Zagros forest cover showed low to moderate correlations. | ||
| کلیدواژه‌ها [English] | ||
| Forest vegetation, Landsat, Principal component analysis (PCA), Sardasht | ||
| مراجع | ||
|
- Ayanlade, A., 2017. Remote sensing vegetation dynamics analytical methods: a review of vegetation indices techniques. Geoinformatica Polonica, 16: 7-17. - Bannari, A., Asalhi, H. and Teillet, P.M., 2002. Transformed difference vegetation index (TDVI) for vegetation cover mapping. Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing Symposium. Toronto, Canada, 24-28 Jun. 2002: 3053-3033. - Biabani, K., Pilevar, B. and Safari, A., 2016. Comparison of spatial patterns and interspecific association of Gall oak (Quercus infectoria Oliv.) and Lebanon oak (Q. libani Oliv.) in two less degraded and degraded oak stands in northern Zagros (Case study: Khedr Abad, Sardasht). Iranian Journal of Forest and Poplar Research, 24(1): 77-88 (In Persian). - Bihamta, M.R. and Zare Chahouki, M.A., 2015. Principles of Statistics for the Natural Resources Science. University of Tehran Press, Tehran, 322p (In Persian). - Birth, G.S. and McVey, G.R., 1968. Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal, 60(6): 640-643. - Boegh, E., Soegaard, H., Broge, N., Hasager, C.B., Jensen, N.O., Schelde, K. and Thomsen, A., 2002. Airborne multispectral data for quantifying leaf area index, nitrogen concentration and photosynthetic efficiency in agriculture. Remote Sensing of Environment, 81(2-3): 179-193. - Chen, J.M., 1996. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22(3): 229-242. - Crippen, R.E., 1990. Calculating the vegetation index faster. Remote Sensing of Environment, 34(1): 71-73. - Es´haghi Rad, J., Motalebpour, A. and Alijanpour, A., 2016. Association survey between oak species in relation to physiographic factors in Zagros forest (Case study: Sardasht forest, Rabat). Forest Research and Development, 1(4): 285-294. - FAO, 2013. Climate change guidelines for forest managers. FAO Forestry Paper No. 172. Food and Agriculture Organization of the United Nations, Rome, 104p. - Gamon, J.A. and Surfus, J.S., 1999. Assessing leaf pigment content and activity with a reflectometer. New Phytologist, 143(1): 105-117. - Garbolino, E., Sanseverino-Godfrin, V. and Hinojos-Mendoza, G., 2017. Reprint of: Describing and predicting of the vegetation development of Corsica due to expected climate change and its impact on forest fire risk evolution. Safety Science, 97: 81-87. - Gitelson, A.A., Stark, R., Grits, U., Rundquist, D., Kaufman, Y. and Derry, D., 1996. Vegetation and soil lines in visible spectral space: a concept and technique for remote estimation of vegetation fraction. International Journal of Remote Sensing, 23(13): 2537-2562. - Goel, N.S. and Qin, W., 1994. Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: A computer simulation. Remote Sensing Reviews, 10(4): 309-347. - Goodarzi, M., Pourhashemi, M. and Azizi, Z., 2019. Investigation on Zagros forests cover changes under the recent droughts using satellite imagery. Journal of Forest Science, 65(1): 9-17. - Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J. and Strachan, I. B., 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3): 337-352. - Hlásny, T., Barka, I., Sitková, Z., Bucha, T., Konôpka, M. and Lukáč, M., 2015. MODIS-based vegetation index has sufficient sensitivity to indicate stand-level intra-seasonal climatic stress in oak and beech forests. Annals of Forest Science, 72(1): 109-125. - Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. and Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2): 195-213. - Huete, A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3): 295-309. - IPCC, 2013. Annex I: Atlas of Global and Regional Climate Projections [van Oldenborgh, G.J., Collins, M., Arblaster, J., Christensen, J.H., Marotzke, J., Power, S.B., Rummukainen M. and Zhou, T. (Eds.)]: 1311-1394. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V. and Midgley, P.M. (Eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535p. - Kauth, R.J. and Thomas, G.S., 1976. The tasseled cap - A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data. West Lafayette, Indiana, 29 Jun.-1 Jul. 1976: 4B41-4B51. - Lobell, D.B. and Asner, G.P., 2003. Hyperion studies of crop stress in Mexico. Proceedings of the 12th Annual JPL Airborne Earth Science Workshop. Pasadena, California, 24-28 Feb. 2003: 6p. - Louhaichi, M., Borman, M.M. and Johnson, D.E., 2001. Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 16(1): 65-70. - Maroufzade, E. and Attarod, P., 2021. Are forest vegetation variations consistent with trends of meteorological parameters in the northern Zagros region of Iran? Iranian Journal of Forest, In press. - Masek, J.G., Hayes, D.J., Joseph Hughes, M., Healey, S.P. and Turner, D.P., 2015. The role of remote sensing in process-scaling studies of managed forest ecosystems. Forest Ecology and Management, 355: 109-123. - Pinty, B. and Verstraete, M.M., 1992. GEMI: A non-linear index to monitor global vegetation from satellites. Vegetatio, 101: 15-20. - Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H. and Sorooshian, S., 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2): 119-126. - Rizzetto, S., Belyazid, S., Gégout, J.C., Nicolas, M., Alard, D., Corcket, E., Gaudio, N., Sverdrup, H. and Probst, A., 2016. Modelling the impact of climate change and atmospheric N deposition on French forests biodiversity. Environmental Pollution, 213: 1016-1027. - Rondeaux, G., Steven, M. and Baret, F., 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2): 95-107. - Roujean, J.L. and Breon, F.M., 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51(3): 375-384. - Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W., 1973. Monitoring vegetation systems in the great plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite–1 Symposium. Washington, D.C., 10-14 Dec. 1973: 309-317. - Sohngen, B. and Tian, X., 2016. Global climate change impacts on forests and markets. Forest Policy and Economics, 72: 18-26. - Souza, R., Feng, X., Antonino, A., Montenegro, S., Souza, E. and Porporato, A., 2016. Vegetation response to rainfall seasonality and interannual variability in tropical dry forests. Hydrological Processes, 30(20): 3583-3595 - Sripada, R.P., Heiniger, R.W., White, J.G. and Meijer, A.D., 2006. Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agronomy Journal, 98(4): 968-977. - Thiele, J.C., Nuske, R.S., Ahrends, B., Panferov, O., Albert, M., Staupendahl, K., … and Saborowski, J., 2017. Climate change impact assessment—a simulation experiment with Norway spruce for a forest district in Central Europe. Ecological Modelling, 346: 30-47. - Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150. - Upgupta, S., Sharma, J., Jayaraman, M., Kumar, V. and Ravindranath, N.H., 2015. Climate change impact and vulnerability assessment of forests in the Indian Western Himalayan region: A case study of Himachal Pradesh, India. Climate Risk Management, 10: 63-76. - Xu, H., 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14): 3025-3033. - Yaghobi, S., Heidarizadi Z. and Mirzapour, H., 2019. Comparing NDVI and RVI for forest density estimation and their relationships with rainfall (Case study: Malekshahi, Ilam Province). Environmental Resources Research, 7(2): 117-128. - Yang, Z., Willis, P. and Mueller, R., 2008. Impact of band-ratio enhanced AWIFS image to crop classification accuracy. Proceedings of the 17th William T. Pecora Memorial Remote Sensing Symposium on The Future of Land Imaging…Going Operational. Denver, Colorado, 18-20 Nov. 2008: 11p. | ||
|
آمار تعداد مشاهده مقاله: 546 تعداد دریافت فایل اصل مقاله: 642 |
||